Looper doesn’t just run pipelines; it can also check and summarize the progress of your jobs, as well as remove all files created by them.

Each task is controlled by one of the five main commands run, summarize, destroy, check, clean.

looperrun: Runs pipelines for each sample, for each pipeline. This will use your compute settings to build and submit scripts to your specified compute environment, or run them sequentially on your local computer.

loopersummarize: Summarize your project results. This command parses all key-value results reported in the each sample stats.tsv and collates them into a large summary matrix, which it saves in the project output directory. This creates such a matrix for each pipeline type run on the project, and a combined master summary table.

loopercheck: Checks the run progress of the current project. This will display a summary of job status; which pipelines are currently running on which samples, which have completed, which have failed, etc.

looperdestroy: Deletes all output results for this project.

Here you can see the command-line usage instructions for the main looper command and for each subcommand: